Knowledge catalogue
arXiv:2604.15958v1 Announce Type: cross Abstract: Despite the considerable promise of Retrieval-Augmented Generation (RAG), many real-world use cases may create privacy concerns, where the purported u
arXiv:2602.22699v2 Announce Type: replace-cross Abstract: SQL is the de facto interface for exploratory data analysis; however, releasing exact query results can expose sensitive information through m
arXiv:2604.15063v1 Announce Type: new Abstract: Gradient inversion attacks threaten client privacy in federated learning by reconstructing training samples from clients' shared gradients. Gradients ag
arXiv:2604.13127v1 Announce Type: new Abstract: The need to selectively and efficiently erase learned information from deep neural networks is becoming increasingly important for privacy, regulatory c
arXiv:2604.12431v1 Announce Type: cross Abstract: Organisations increasingly outsource privacy-sensitive data transformations to cloud providers, yet no practical mechanism lets the data owner verify
arXiv:2604.11146v1 Announce Type: new Abstract: Federated Learning (FL) enables collaborative model training across distributed clients without sharing raw data, thereby preserving privacy. However, F
arXiv:2604.10819v1 Announce Type: cross Abstract: A recent line of work initiated by Chiesa and Gur and further developed by Herman and Rothblum investigates the sample and communication complexity of
arXiv:2511.22599v2 Announce Type: replace-cross Abstract: Deploying Large Language Model (LLM) services at the edge benefits latency-sensitive and privacy-aware applications. However, the stateless na
arXiv:2604.06795v1 Announce Type: cross Abstract: Federated Learning (FL) enables decentralized model training across multiple clients without exposing private data, making it ideal for privacy-sensit
arXiv:2604.06297v1 Announce Type: cross Abstract: Given the growing reliance on private data in training Large Language Models (LLMs), Federated Learning (FL) combined with Parameter-Efficient Fine-Tu
arXiv:2604.06631v1 Announce Type: cross Abstract: Federated Learning (FL) enables collaborative model training while preserving data privacy, but its practical deployment is hampered by system and sta
arXiv:2610.10334v1 Announce Type: new Abstract: Face de-identification (FDeID) has emerged as a critical privacy-preserving technology, yet its evaluation remains fundamentally fragmented. Existing pr
arXiv:2610.08464v1 Announce Type: cross Abstract: Learned surgical simulators and world models can roll out plausible procedural futures, but they carry no grounded estimate of how often interventiona
arXiv:2610.05863v1 Announce Type: new Abstract: High-quality child care in early life is a critical determinant of children's growth and development. Research on child care quality has been constraine
arXiv:2610.06636v1 Announce Type: new Abstract: Many machine learning applications involve sensitive data and therefore require training under differential privacy (DP). However, DP training often deg
arXiv:2610.05476v1 Announce Type: new Abstract: Scaling decentralized learning changes not only the number of clients N, but also the dynamics of information propagation and consensus. We argue that t
arXiv:2510.07304v2 Announce Type: replace-cross Abstract: Machine learning (ML) models memorize and leak training data, causing serious privacy issues to data owners. Training algorithms with differen
arXiv:2610.02865v1 Announce Type: new Abstract: Time-series forecasting is widely used in sensitive domains. Models in these settings are often trained on longitudinal user- or entity-level records, w
arXiv:2601.22169v2 Announce Type: replace-cross Abstract: Humans are susceptible to undesirable behaviours and privacy leaks under the influence of alcohol. This paper investigates drunk language, i.e
arXiv:2609.40335v1 Announce Type: new Abstract: Differentially Private Stochastic Gradient Descent (DP-SGD) is a leading approach for privacy-preserving fine-tuning of large language models (LLMs). Ma
arXiv:2609.38925v1 Announce Type: new Abstract: Multimodal federated learning (MFL) has emerged as a pivotal paradigm for leveraging distributed data to enhance model performance. However, existing me
arXiv:2609.39684v1 Announce Type: new Abstract: Privacy has become an increasingly important concern in the Document Analysis community, to the extent that in many environments such as archives, gover
arXiv:2609.34790v2 Announce Type: replace-cross Abstract: LLM agents operate in persistent collaborative environments involving multiple users, communities, memories, files, and tools. Community bound
arXiv:2609.38006v1 Announce Type: new Abstract: Running a language model locally offers advantages in privacy, latency, and cost, but local hardware fits only small models, which are less capable than
arXiv:2609.29191v1 Announce Type: new Abstract: Sensitive information is defined by domain and intent, not a universal category, yet redaction systems such as privacy filters and named-entity recogniz
arXiv:2609.28695v1 Announce Type: new Abstract: This work presents a federated framework for training Averaged n-Dependence Estimators (AnDE) in distributed environments. The proposed method focuses o
arXiv:2609.30258v1 Announce Type: new Abstract: Distributed learning in embodied reinforcement-learning agents offers a degree of privacy by retaining raw sensor data on-device and transmitting only p
arXiv:2609.27658v1 Announce Type: new Abstract: Private decentralized learning is affected by sampling noise, privacy noise, and decentralized bias under heterogeneous data. We propose Private Recursi
arXiv:2609.27202v1 Announce Type: cross Abstract: The growing deployment of Internet of Things (IoT) devices has increased the need for privacy-preserving intrusion detection systems that operate dire
arXiv:2508.20443v3 Announce Type: replace Abstract: Large language models (LLMs) are trained on massive datasets that may include private or copyrighted content. Due to growing privacy and ownership c
arXiv:2609.22783v1 Announce Type: new Abstract: We study differentially private covariance estimation in operator norm for mean-zero sub-Gaussian distributions with unknown covariance support and at m
arXiv:2510.24233v2 Announce Type: replace Abstract: Deep generative models are often trained on sensitive data, such as genetic sequences, health data, or more broadly, any copyrighted, licensed or pr
Automated lint: 14 errors, 54840 warnings, 3 info
arXiv:2609.16496v1 Announce Type: cross Abstract: AI policies introduced by software organisations to mitigate LLM-related risks such as sensitive information leaks and unauthorised usage are not usef
arXiv:2609.17204v1 Announce Type: cross Abstract: Wi-Fi signal data can be used to compromise the privacy of individuals. While many existing approaches rely on Channel State Information (CSI), collec
arXiv:2609.12470v1 Announce Type: new Abstract: Customer churn is one of the major issues in the telecommunication industry. To predict customer churn, conventional centralized machine learning approa
arXiv:2609.12448v1 Announce Type: new Abstract: Sensitive attributes such as age, income, and occupation can be inferred from user-generated content by aggregating indirect cues across many ordinary p
arXiv:2609.11940v1 Announce Type: cross Abstract: The rapid diffusion of generative artificial intelligence raises privacy, latency, and performance concerns that motivate a shift toward 'local-first'
Automated lint: 12 errors, 54839 warnings, 3 info
arXiv:2404.13381v2 Announce Type: replace Abstract: The COVID19 pandemic had enormous economic and societal consequences. Contact tracing is an effective way to reduce infection rates by detecting pot
arXiv:2609.09166v1 Announce Type: new Abstract: This paper investigates collaborative speculative decoding (CoSD), a distributed large language model (LLM) inference framework in which an on-device sm
Automated lint: 12 errors, 53990 warnings, 3 info
arXiv:2609.05770v1 Announce Type: new Abstract: Differentially private (DP) fine-tuning methods treat sparse Mixture-of-Experts (MoE) models as a single dense block, ignoring that shared layers see al
arXiv:2609.05403v1 Announce Type: cross Abstract: Retail search systems serve diverse geographic regions with distinct query patterns, vocabularies, and product preferences, creating significant data
arXiv:2609.02984v1 Announce Type: new Abstract: The conventional approach to machine learning, that is, collecting data, training models, and performing inference in a single location, faces fundament
arXiv:2609.03972v1 Announce Type: new Abstract: Label removal occurs frequently in classification systems with evolving taxonomies, where categories must be dynamically updated or eliminated. To accom
arXiv:2609.00873v1 Announce Type: new Abstract: Diffusion language models (DLMs) have recently emerged as an alternative modeling paradigm to autoregressive LMs, offering advantages such as parallel g
arXiv:2608.28312v1 Announce Type: cross Abstract: Multimodal large language models (MLLMs) can memorize identity-specific facts about people in their fine-tuning data, creating privacy risks when a pe
arXiv:2608.26743v1 Announce Type: new Abstract: Enterprises fine-tune language models on proprietary data that may later require removal due to privacy, contractual, or compliance obligations. Selecti
arXiv:2602.06110v2 Announce Type: replace Abstract: Publicly available clinical machine learning models pose an underappreciated privacy risk: their parameters or outputs can be exploited to recover i
arXiv:2608.27360v1 Announce Type: new Abstract: Speaker anonymization suppresses speaker-identifying attributes from speech while preserving linguistic content and quality. We propose repurposing XTTS
arXiv:2608.18634v1 Announce Type: new Abstract: Federated Active Learning (FAL) addresses the dual challenges of data privacy and label scarcity, where the absence of a global data view introduces add
arXiv:2602.12250v2 Announce Type: replace Abstract: Graph neural networks (GNNs) enable powerful unsupervised learning of communities. However, such inference may inadvertently expose sensitive group
arXiv:2507.19894v2 Announce Type: replace Abstract: With the rapid advancement of generative models, privacy, copyright, safety, and reliability risks have attracted growing attention. To mitigate the
arXiv:2411.10023v3 Announce Type: replace Abstract: Deep neural networks have enabled numerous studies and applications on both Euclidean data, such as images and text, and non-Euclidean data, such as
arXiv:2608.13844v1 Announce Type: cross Abstract: Large language models (LLMs) have become core components of cloud-based intelligent services in academia and industry, yet their training and deployme
arXiv:2601.21628v2 Announce Type: replace-cross Abstract: Diffusion models have achieved remarkable progress in image generation, but their increasing deployment raises serious concerns about privacy
arXiv:2608.12100v1 Announce Type: cross Abstract: In high-stakes applications, reliable confidence estimates are as important as the predictions themselves. Confidence calibration ensures that predict
arXiv:2607.15606v2 Announce Type: replace Abstract: Synthetic sequential tabular data are increasingly used for privacy-preserving data sharing and data-driven research, but evaluating their fidelity
arXiv:2603.10937v2 Announce Type: replace Abstract: The use of synthetic data has become increasingly popular as a privacy-preserving alternative to sharing real datasets, especially in sensitive doma